So sánh phương pháp

	Mô hình Chủ đề NMF Có thể Giải thích	Phân loại dựa trên BERT có thể giải thích
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2001 (NMF); XAI integration ~2017–present	2019–2020
Người khởi xướng	Lee, D. D. & Seung, H. S. (NMF); XAI layer attributed to community practice post-2016	Devlin et al. (BERT); explainability methods by Lundberg & Lee (SHAP), Ribeiro et al. (LIME), Sundararajan et al. (Integrated Gradients)
Loại	Interpretable unsupervised topic model	Pre-trained transformer classifier with post-hoc or intrinsic explainability
Công trình gốc	Lee, D. D., & Seung, H. S. (2001). Algorithms for non-negative matrix factorization. Advances in Neural Information Processing Systems, 13, 556–562.	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of NAACL-HLT 2019, pp. 4171–4186.
Tên gọi khác	XAI-NMF, interpretable NMF topic model, explainable NMF, transparent NMF topic modeling	XAI-BERT, interpretable BERT classifier, BERT with post-hoc explanation, transparent BERT classification
Liên quan	6	6
Tóm tắt	An Explainable NMF Topic Model combines Non-negative Matrix Factorization — a parts-based decomposition of a document-term matrix — with explicit interpretability techniques such as coherence metrics, word contribution scores, and SHAP-style attribution to make discovered topics transparent and auditable by human readers.	Explainable BERT-based Classification combines the predictive power of fine-tuned BERT transformers for text classification with post-hoc or intrinsic explainability techniques — such as SHAP, LIME, attention analysis, or integrated gradients — to reveal which words or tokens drove each prediction. The result is a classifier that is both accurate and interpretable enough for high-stakes or auditable NLP applications.